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Junrong Zhang

Publications and source records attributed to Junrong Zhang.

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Hydrodynamically engineered Indigenous arrows skip on water for waterfowl hunting

Across the Northern Hemisphere, Indigenous hunters developed arrows capable of skipping across the water surface to strike waterfowl. Archaeological and ethnographic records reveal remarkably similar projectile designs spanning millennia and geographically distant cultures, suggesting a convergent technological solution. Despite extensive study of water-entry dynamics, the physical principles underlying this behaviour remain poorly understood. Here we show that successful water-skipping arises from a small set of coupled geometric and dynamical parameters that define a bounded operational regime separating rebound, plunging, and overshoot. Using a combination of controlled experiments, hydrodynamic modeling, and historical reconstruction, we demonstrate that reconstructed arrow designs from independent cultures consistently fall within this predicted regime. These results demonstrate that Indigenous technologies were effectively tuned to satisfy the hydrodynamic constraints governing controlled skipping, providing evidence of convergent optimization in human-engineered systems. More broadly, our results suggest that material culture encodes physical knowledge that formal science is only beginning to articulate, and that the archaeological record and Indigenous culture may be an underexplored archive of empirical discovery.

physics.flu-dyn

Rongzai agent: A Large Language Model-Based Autonomous Assistant for Rietveld Refinement of Neutron Diffraction Data

Neutron diffraction (ND) is an indispensable technique for determining atomic positions (especially light elements) and thus serves as a critical probe for revealing microscopic structures in materials science. However, traditional Rietveld refinement of ND data relies heavily on manual operation of specialized software, which is time-consuming, labor-intensive, and highly dependent on user expertise, severely hindering automated analysis. The automation of Rietveld refinement has long been a long-standing and challenging problem in crystallography. To address this challenge, this paper presents the Dr.Sai-Rongzai agent, an autonomous refinement assistant based on a large language model (LLM), a specialist knowledge base, and the GSAS-II refinement engine, achieving for the first time an intelligent refinement that integrates knowledge-driven decision-making. The agent accomplishes a fully automated workflow from natural language task parsing to autonomous decision-making, execution of refinement strategies, and report generation. Evaluation on five representative samples shows that the Rongzai agent achieves lower Rwp values than human specialists on three samples (2.88% vs. 4.42%, 5.06% vs. 5.40%, 7.60% vs. 9.00%), while on the other two samples its results are very close to those of the specialists. The agent is currently deployed at the China Spallation Neutron Source (CSNS) and is open for external user registration, providing an intelligent and user-friendly analytical tool for materials research. This work fully leverages the cutting-edge advantages of LLM, offers a new path to solve the long-standing problem of automated refinement, takes a key step toward intelligent and fully automated crystallographic analysis, and holds great potential to accelerate AI for Science discoveries in neutron-based materials characterization.

cond-mat.mtrl-sci

Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws

In this paper, a dataset of one-dimensional powder diffraction patterns was designed with new strategy to train Convolutional Neural Networks for predicting space groups. The diffraction pattern was calculated based on lattice parameters and Extinction Laws, instead of the traditional approach of generating it from a crystallographic database. This paper demonstrates that the new strategy is more effective than the conventional method. As a result, the model trained on the cubic and tetragonal training set from the newly designed dataset achieves prediction accuracy that matches the theoretical maximums calculated based on Extinction Laws. These results demonstrate that machine learning-based prediction can be both physically reasonable and reliable. Additionally, the model trained on our newly designed dataset shows excellent generalization capability, much better than the one trained on a traditionally designed dataset.

cs.NE

On the Jets Induced by a Cavitation Bubble Near a Cylinder

The dynamics of cavitation bubbles in the vicinity of a solid cylinder or fibre are seen in water treatment, demolition and/or cleaning of composite materials, as well as bio-medical scenarios such as ultrasound-induced bubbles near the tubular structures in the body. When the bubble collapses near the surface, violent fluid jets may be generated. Understanding whether these jets occur and predicting their directions -- departing or approaching the solid surface -- is crucial for assessing their potential impact on the solid phase. However, the criteria for classifying the onset and directions of the jets created by cavitation near a curved surface of a cylinder have not been established. In this research, we present models to predict the occurrence and directions of the jet in such scenarios. The onset criteria and the direction(s) of the jets are dictated by the bubble stand-off distance and the cylinder diameter. Our models are validated by comprehensive experiments. The results not only predict the jetting behaviour but can serve as guidelines for designing and controlling the jets when a cavitation bubble collapses near a cylinder, whether for protective or destructive purposes.

physics.flu-dyn

Prediction of superconductivity and topological aspects in single-layer $β$-Bi$_{2}$Pd

Topological superconductors, characterized by topologically nontrivial states residing in a superconducting gap, are a recently discovered class of materials having Majorana Fermions. The interplay of superconductivity and topological states give rise to opportunities for achieving such topological superconductors in condensed matter systems. Up to now, several single-material topological superconductors in this form have been theoretically predicted and experimentally confirmed. Here, using the first-principles calculations, we study the superconducting single-layer $β$-Bi$_{2}$Pd. The electronic density of states near Fermi level of this monolayer are dominated by the Bi-p and Pd-d orbitals, forming a two-band Fermi surface with multi-class sheets. The presence of soft phonon bands, in cooperation with the electron susceptibility, account for electron-phonon superconductivity of single-layer $β$-Bi$_{2}$Pd. With the centrosymmetric structure, single-layer $β$-Bi$_{2}$Pd possesses a continuous gap over the whole Brillouin zone and topological Dirac-like states at its one-dimensional boundary. The present findings would lead to the expectation of one-dimensional topological superconductivity and Majorana bound states in monolayer candidate of $β$-Bi$_{2}$Pd with intrinsic full-gap superconductivity.

cond-mat.mtrl-sci

Insight into the origin of Lithium/Nickel ions exchange in layered Li(NixMnyCoz)O2 cathode materials

In layered LiNixMnyCozO2 cathode material for lithium-ion batteries, the spins of transition metal (TM) ions construct a two-dimensional triangular networks, which can be considered as a simple case of geometrical frustration. By performing neutron powder diffraction experiments and magnetization measurements, we find that long-range magnetic order cannot be established in LiNixMnyCozO2 even at low temperature of 3 K. Remarkably, the frustration parameters of these compounds are estimated to be larger than 30, indicating the existence of strongly frustrated magnetic interactions between spins of TM ions. As frustration will inevitably give rise to lattice instability, the formation of Li/Ni exchange in LiNixMnyCozO2 will help to partially relieve the degeneracy of the frustrated magnetic lattice by forming a stable antiferromagnetic state in hexagonal sublattice with nonmagnetic ions located in centers of the hexagons. Moreover, Li/Ni exchange will introduce 180° superexchange interaction, which further relieves the magnetic frustration through bringing in new exchange paths. Thus, the variation of Li/Ni exchange ratio vs. TM mole fraction in LiNixMnyCozO2 with different compositions can be well understood and predicted in terms of magnetic frustration and superexchange interactions. This provides a unique viewpoint to study the Li/Ni ions exchange in layered Li(NixMnyCoz)O2 cathode materials.

cond-mat.mtrl-sci

Hexagonal Ti2B2 monolayer: A Promising Anode Material Offering High Rate Capability for Li-Ion and Na-Ion Batteries

Combining first-principles density functional method and crystal structure prediction techniques, we report a series of hexagonal two-dimensional (2D) transition metal borides (TMBs) including Sc2B2, Ti2B2, V2B2, Cr2B2, Y2B2, Zr2B2, and Mo2B2. Their dynamic and thermal stabilities are testified by phonon and molecular dynamics simulations. We investigate the potential of 2D Ti2B2 monolayer as the anode material for Li-ion batteries (LIBs) and Na-ion batteries (NIBs). The Ti2B2 monolayer possesses high theoretical specific capacities of 456 and 1027 mAhg-1 for Li and Na, respectively. The very high Li/Na diffusivity with ultralow energy barrier of 0.017/0.008 eV indicates an excellent charge-discharge capability. In addition, the good electronic conductivity during the whole lithiation process is found by electronic structure calculations. The very small change in volume after the adsorption of one, two, and three layers of Li and Na ions indicates that the Ti2B2 monolayer is robust. These results highlight the suitability of Ti2B2 monolayer as well as the other 2D TMBs as excellent anode materials for both LIBs and NIBs.

cond-mat.mtrl-sci

The Virtual Neutron Experiment for TOF Neutron Reflectometer

A general virtual neutron experiment for TOF neutron reflectometer was introduced, including instrument simulation, sample modeling, detector simulation and data reduction to mimic the routine of real experimental process and data reduction. The reduced data fit quite well with the sample simulation, confirming the reliability of our algorithm and the smearing effect was analyzed. Methodology and algorithms developed in this study paves the way for the future development of data processing and data reduction software for the TOF neutron reflectometers in China Spallation Neutron Source (CSNS). Details of the virtual experiment and performance of the instrument was demonstrated.

physics.ins-det